In-Vehicle Telltale Diagnosis Using Icon and Background Correlation
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Solution Overview
Problem
Conventional in-vehicle telltale failure diagnosis systems inaccurately determine icon images as failures due to image quality degradation and hue changes caused by noise during image processing, leading to false failure determinations.
Innovation Solution
An in-vehicle telltale failure diagnosis device and method that calculates correlation values between the processed icon and background parts of an image with reference correct-answer images, using separate threshold values and frame-based filtering to distinguish genuine failures from noise-induced changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional correlation-based failure determination is used, then failure detection capability is improved, but false failure determination occurs due to image quality degradation and hue changes from noise
Solution Approach 1:
The patent segments the image into icon regions and background regions, applying different correlation determination methods to each. Icon regions use correlation against reference correct-answer icon images, while background regions use correlation against predetermined tonal colors. This segmentation allows tailored noise tolerance for each region type, reducing false failures from background noise while maintaining icon detection accuracy.
Solution Approach 2:
The patent changes the reference parameter for background correlation from specific background images to predetermined tonal colors. This parameter change makes the background correlation calculation inherently more tolerant of noise-induced hue changes and quality degradation, as tonal color comparison is less sensitive to the types of noise artifacts that affect detailed image comparison.
2Difficulty of detecting and measuring
If strict correlation matching is applied, then detection sensitivity is improved, but false positives increase due to noise-induced image variations
Solution Approach 1:
The patent applies different correlation thresholds and reference methods to different regions: icon regions use strict correlation matching against reference icons to maintain high detection sensitivity, while background regions use more tolerant correlation against tonal colors to reduce false positives. This local quality differentiation allows each region to be evaluated with appropriate stringency.
Solution Approach 2:
The patent introduces predetermined tonal colors as an intermediary reference for background correlation, rather than directly comparing against specific background images. This intermediary approach acts as a buffer that absorbs noise variations, allowing the system to maintain detection sensitivity while reducing false positives from noise-induced variations.
Data Source
AI summary
An in-vehicle telltale failure diagnosis device includes: a correct-answer icon area memory controller that obtains and stores a reference correct-answer icon image; an icon image cutter that cuts out an icon image from a combined image; an icon correlation calculator that calculates a first correlation value between an icon in the icon image that has been cut out and an icon in the reference correct-answer icon image; a background correlation calculator that calculates a second correlation value between a background part excepting the icon and a predetermined tonal color; and an icon failure determiner that determines, based on the first correlation value, that the icon in the icon image is a failure, and a background failure determiner that determines, based on the second correlation value, that the background part in the icon image is a failure.